Abstract
Influential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential influence spread that can be generated by these influential nodes is hidden behind the structures of OSNs, which are often held by OSN providers and unavailable to advertisers for privacy concerns. A social advertising model known as influencer marketing is to have OSN providers offer and price candidate nodes for advertisers to purchase for seeding marketing campaigns. In this setting, a reasonable price profile for the candidate nodes should effectively reflect the expected influence gain they can bring in a marketing campaign.
In this paper, we study the problem of pricing the influential nodes based on their expected influence spread to help advertisers select the initiators of marketing campaigns without the knowledge of OSN structures. We design a function characterizing the divergence between the price and the expected influence of the initiator sets. We formulate the problem to minimize the divergence and derive an optimal price profile. An advanced algorithm is developed to estimate the price profile with accuracy guarantees. Experiments with real OSN datasets show that our pricing algorithm can significantly outperform other baselines.
- C. Aslay, F. Bonchi, L. V. Lakshmanan, and W. Lu. Revenue maximization in incentivized social advertising. PVLDB, 10(11):1238--1249, 2017. Google ScholarDigital Library
- C. Aslay, W. Lu, F. Bonchi, A. Goyal, and L. V. Lakshmanan. Viral marketing meets social advertising: Ad allocation with minimum regret. PVLDB, 8(7):814--825, 2015. Google ScholarDigital Library
- C. Borgs, M. Brautbar, J. Chayes, and B. Lucier. Maximizing social influence in nearly optimal time. In Proc. SODA, pages 946--957, 2014. Google ScholarCross Ref
- P. Chalermsook, A. Das Sarma, A. Lall, and D. Nanongkai. Social network monetization via sponsored viral marketing. ACM SIGMETRICS Performance Evaluation Review, 43(1):259--270, 2015. Google ScholarDigital Library
- W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proc. ACM KDD, pages 1029--1038, 2010. Google ScholarDigital Library
- W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. In Proc. ACM KDD, pages 199--208, 2009. Google ScholarDigital Library
- W. Chen, Y. Yuan, and L. Zhang. Scalable influence maximization in social networks under the linear threshold model. In Proc. IEEE ICDM, pages 88--97, 2010. Google ScholarDigital Library
- S. Cheng, H. Shen, J. Huang, W. Chen, and X. Cheng. Imrank: influence maximization via finding self-consistent ranking. In Proc. ACM SIGIR, pages 475--484, 2014. Google ScholarDigital Library
- F. Chung and L. Lu. Concentration inequalities and martingale inequalities: a survey. Internet Mathematics, 3(1):79--127, 2006.Google ScholarCross Ref
- E. Cohen, D. Delling, T. Pajor, and R. F. Werneck. Sketch-based influence maximization and computation: Scaling up with guarantees. In Proc. ACM CIKM, pages 629--638, 2014. Google ScholarDigital Library
- P. Dagum, R. Karp, M. Luby, and S. Ross. An optimal algorithm for monte carlo estimation. SIAM Journal on Computing, 29(5):1484--1496, 2000. Google ScholarDigital Library
- P. Domingos and M. Richardson. Mining the network value of customers. In Proc. ACM KDD, pages 57--66, 2001. Google ScholarDigital Library
- eMarketer. Social network ad spending worldwide, by region, 2013-2017. https://www.emarketer.com/Chart/Social-Network-Ad-Spending-Worldwide-by-Region-2013-2017/168356, 2015.Google Scholar
- eMarketer. Influencer marketing is about data, not celebrity deals. https://www.emarketer.com/Article/Influencer-Marketing-About-Data-Not-Celebrity-Deals/1016683, 2017.Google Scholar
- eMarketer. How much are brands paying influencers? https://www.emarketer.com/content/how-much-are-brands-paying-influencers, 2019.Google Scholar
- eMarketer. Is everyone on instagram an influencer? https://www.emarketer.com/content/is-everyone-on-instagram-an-influencer, 2019.Google Scholar
- https://newsroom.fb.com/company-info/.Google Scholar
- https://famebit.com/.Google Scholar
- K. Han, K. Huang, X. Xiao, J. Tang, A. Sun, and X. Tang. Efficient algorithms for adaptive influence maximization. PVLDB, 11(9):1029--1040, 2018. Google ScholarDigital Library
- K. Huang, J. Tang, K. Han, X. Xiao, W. Chen, A. Sun, X. Tang, and A. Lim. Efficient approximation algorithms for adaptive influence maximization. The VLDB Journal, 2020.Google ScholarCross Ref
- K. Huang, J. Tang, X. Xiao, A. Sun, and A. Lim. Efficient approximation algorithms for adaptive target profit maximization. In Proc. IEEE ICDE, pages 649--660, 2020.Google Scholar
- I. M. Hub. The remarkable rise of influencer marketing. https://influencermarketinghub.com/the-rise-of-influencer-marketing/, 2020.Google Scholar
- D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In Proc. ACM KDD, pages 137--146, 2003. Google ScholarDigital Library
- A. Khan, B. Zehnder, and D. Kossmann. Revenue maximization by viral marketing: A social network host's perspective. In Proc. IEEE ICDE, pages 37--48, 2016.Google ScholarCross Ref
- H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In Proc. WWW, pages 591--600, 2010. Google ScholarDigital Library
- J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In Proc. ACM KDD, pages 420--429, 2007. Google ScholarDigital Library
- W. Lu and L. V. Lakshmanan. Profit maximization over social networks. In Proc. IEEE ICDM, pages 479--488, 2012. Google ScholarDigital Library
- H. T. Nguyen, T. P. Nguyen, T. N. Vu, and T. N. Dinh. Outward influence and cascade size estimation in billion-scale networks. In Proc. ACM SIGMETRICS, pages 63--63, 2017. Google ScholarDigital Library
- H. T. Nguyen, M. T. Thai, and T. N. Dinh. Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks. In Proc. ACM SIGMOD, pages 695--710, 2016. Google ScholarDigital Library
- Reuters. Social media ad spending is expected to pass newspapers by 2020. http://fortune.com/2016/12/05/social-media-ad-spending-newspapers-zenith-2020/, 2016.Google Scholar
- M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In Proc. ACM KDD, pages 61--70, 2002. Google ScholarDigital Library
- J. Tang, K. Huang, X. Xiao, L. V. Lakshmanan, X. Tang, A. Sun, and A. Lim. Efficient approximation algorithms for adaptive seed minimization. In Proc. ACM SIGMOD, pages 1096--1113, 2019. Google ScholarDigital Library
- J. Tang, X. Tang, X. Xiao, and J. Yuan. Online processing algorithms for influence maximization. In Proc. ACM SIGMOD, pages 991--1005, 2018. Google ScholarDigital Library
- J. Tang, X. Tang, and J. Yuan. Profit maximization for viral marketing in online social networks. In Proc. IEEE ICNP, pages 1--10, 2016.Google ScholarCross Ref
- J. Tang, X. Tang, and J. Yuan. Influence maximization meets efficiency and effectiveness: A hop-based approach. In Proc. IEEE/ACM ASONAM, pages 64--71, 2017. Google ScholarDigital Library
- J. Tang, X. Tang, and J. Yuan. An efficient and effective hop-based approach for inluence maximization in social networks. Social Network Analysis and Mining, 8(10), 2018.Google Scholar
- J. Tang, X. Tang, and J. Yuan. Profit maximization for viral marketing in online social networks: Algorithms and analysis. IEEE Transactions on Knowledge and Data Engineering, 30(6):1095--1108, 2018.Google ScholarCross Ref
- J. Tang, X. Tang, and J. Yuan. Towards profit maximization for online social network providers. In Proc. IEEE INFOCOM, pages 1178--1186, 2018.Google ScholarCross Ref
- Y. Tang, Y. Shi, and X. Xiao. Influence maximization in near-linear time: A martingale approach. In Proc. ACM SIGMOD, pages 1539--1554, 2015. Google ScholarDigital Library
- Y. Tang, X. Xiao, and Y. Shi. Influence maximization: Near-optimal time complexity meets practical efficiency. In Proc. ACM SIGMOD, pages 75--86, 2014. Google ScholarDigital Library
Recommendations
Optimal price profile for influential nodes in online social networks
AbstractInfluential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential influence spread that can be generated by these influential nodes is hidden behind the structures ...
Budgetary Effects on Pricing Equilibrium in Online Markets
AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent SystemsFollowing the work of Babaioff et al, we consider the pricing game with strategic vendors and a single buyer, modeling a scenario in which multiple competing vendors have very good knowledge of a buyer, as is common in online markets. We add to this ...
Online Social Advertising via Influential Endorsers
In recent years, many Web-based services such as Facebook and MySpace have been making great progress and creating new opportunities. Because online advertising is the main business model for social networking sites, in this paper we propose a social ...
Comments